Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.

ABSTRACT: In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learnin

SUBMITTER: Romer M 

PROVIDER: S-EPMC4022579 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature